Personalized Medicine: Development of a Predictive Computational Model for Personalized Therapeutic Interventions
Year: 2013
Type: article
Abstract: Lung cancer is the leading cause of cancer-related deaths among men and women. Non-Small Cell Lung Cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. In the past decade, discovery of Epidermal Growth Factor Receptor (EGFR) mutations have heralded a new paradigm of personalized treatment for NSCLC. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Despite these advances, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. This thesis demonstrates the development of a predictive computational model for personalized therapeutic interventions in advanced NSCLC. The findings suggest that the combination of clinical and genetic data significantly improves the model’s predictive performance for tumor response than clinical data alone.
Source: DalSpace (Dalhousie University)
Author: Nelofar Kureshi
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2098025062